Multi-Channel Deep Weighted Aggregation for Semantic Segmentation
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Solution Overview
Problem
Traditional image semantic segmentation algorithms struggle to balance segmentation accuracy and reasoning speed, with most methods either sacrificing precision for speed or vice versa, limiting their effectiveness in complex real-world environments.
Innovation Solution
An image semantic segmentation algorithm based on multi-channel deep weighted aggregation, utilizing a three-channel semantic representation model that includes a low-level, auxiliary, and high-level semantic channel, with weighted aggregation and enhanced training to fuse complementary semantic features, improving accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the size of the image is limited or redundant channels of the network are pruned to reduce computational complexity, then the reasoning speed is improved, but the segmentation accuracy deteriorates
Solution Approach 1:
The patent divides the semantic segmentation task into three independent semantic channels (low-level, auxiliary, and high-level), each processing different aspects of semantic information. This segmentation allows parallel processing that maintains speed while preserving comprehensive feature extraction capabilities for accurate segmentation.
Solution Approach 2:
The patent introduces a multi-dimensional semantic feature extraction approach by creating three distinct semantic channels that operate in different feature spaces. This dimensional expansion enables the system to process diverse semantic information simultaneously without increasing computational burden on a single pathway.
2Manufacturing precision
If U-shaped structure is used to operate on high resolution feature map to improve segmentation accuracy, then the segmentation accuracy is improved, but the computational complexity is greatly increased, making the reasoning speed too slow
Solution Approach 1:
Instead of using a single U-shaped structure that processes all features at high resolution, the patent segments the processing into three semantic channels that operate at different resolution levels and feature abstractions, reducing the overall computational complexity while maintaining segmentation accuracy.
Solution Approach 2:
The patent applies different processing qualities to different semantic channels: low-level semantic channel processes high-resolution features for precise localization, while high-level semantic channel processes lower-resolution abstract features for global context, optimizing the balance between accuracy and speed in different feature spaces.
3Manufacturing precision
If two-branch structure with spatial information is used to improve segmentation accuracy while ensuring speed, then segmentation accuracy is improved to a certain extent, but the mutual independence between branches limits the learning ability, preventing better learning of global semantic features
Solution Approach 1:
The patent creates three semantic channels that serve multiple functions: each channel extracts specific semantic information while collectively they capture comprehensive global and local features. The auxiliary semantic channel specifically bridges local and global information, enabling the system to learn both detailed and contextual features simultaneously.
Solution Approach 2:
The patent implements feedback mechanisms where the auxiliary semantic channel receives and processes information from both low-level and high-level channels, creating a feedback loop that enhances the learning of global semantic features while maintaining the speed benefits of the streamlined architecture.
4Manufacturing precision
If multi-branch structure with information interaction between branches is used to extract semantic features, then segmentation accuracy is improved to a certain extent, but the same multi-branch structure limits the extraction of diverse semantic information, preventing better acquisition of global semantic and context information
Solution Approach 1:
The patent assigns different functional qualities to each semantic channel: the low-level semantic channel focuses on local detailed features, the high-level semantic channel focuses on global contextual features, and the auxiliary semantic channel specializes in transition and interaction features. This differentiated quality assignment enables diverse semantic information extraction within the multi-branch structure.
Solution Approach 2:
The patent expands the feature extraction capability by introducing three distinct semantic dimensions (low-level, auxiliary, high-level) that capture different aspects of semantic information. This dimensional expansion allows the system to extract diverse semantic features simultaneously, overcoming the limitations of conventional multi-branch structures.
Data Source
AI summary
An image semantic segmentation algorithm and system based on multi-channel deep weighted aggregation where the image semantic segmentation algorithm is based on multi-channel deep weighted aggregation. The aggregation includes semantic features with definite class information in an image, transition semantic features between low-level semantic and high-level semantic, and semantic features of context logic relationship in an image are extracted by a low-level semantic channel, an auxiliary semantic channel and a high-level semantic channel, respectively. The aggregation further includes three different semantic features obtained in S1 are fused by weighted aggregation to obtain global semantic information of the image; S3, the semantic features output from respective semantic channels in S1 and the global semantic information in S2 are used to compute loss function for training.